Method for detecting existence of dust spots in digital images based on locally adaptive thresholding

ABSTRACT

A method for detecting a dust spot ( 16 ) in a digital image ( 10 ) includes the steps of determining ( 226 ) an expected dust spot configuration ( 344 ) of the dust spot ( 16 ) in the image ( 10 ); applying ( 228 ) a statistic order filter to a value channel of an HSV color space of the image to generate a filtered value for each pixel being evaluated ( 349 ), the filtered value being based upon the expected dust spot configuration ( 344 ); and comparing ( 230 ) the filtered value to an actual color space value of a plurality of pixels ( 348 ) in the digital image ( 10 ) to generate ( 232 ) a binary image ( 350 ). In one embodiment, the method can also include the step of comparing ( 234 ) the binary image ( 350 ) to the expected dust spot configuration ( 344 ) to determine a probability of the presence of the dust spot ( 16 ) in the digital image ( 10 ).

BACKGROUND

Cameras are commonly used to capture an image of a scene that includesone or more objects. Many digital cameras include an imaging sensorwhich may have a filter positioned near the sensor. Unfortunately, dustparticles can become attached to the filter and can consequently producedark spots (also referred to as “dust spots”) in a captured image. Thisproblem can be relatively common, particularly with cameras that haveinterchangeable lenses. Detection of dust particles is important to givea photographer early warning that the sensor and/or filter needcleaning, or to allow automatic removal of dust spots in capturedimages.

Currently, certain conventional cameras can detect and/or remove dustspots in captured images. However, in these types of cameras, acalibration image of a flat field (such as a blank wall or a sky) thatclearly shows the presence of dust particles may be required forcreating a dust map. In an alternative method, multiple images takenconsecutively are used to create a dust map. Unfortunately, theseconventional methods either require an entirely flat field image ormultiple images to generate the requisite dust map.

SUMMARY

The present invention is directed to a method for detecting a dust spotin a digital image. In one embodiment, the method includes the steps ofdetermining an expected dust spot configuration of the dust spot in thedigital image; applying a statistic order filter to a brightness,luminance, or another similar color space channel, the filtered valuebeing based at least partially upon the expected dust configuration; foreach pixel, comparing the filtered value to an actual color spacechannel value of the same pixel to generate a binary image of thedigital image; and comparing the binary image to the expected dust spotconfiguration to determine the probability of the presence of the dustspot at each location in the digital image.

In one embodiment, the step of determining includes basing the expecteddust spot configuration at least partially on an f-stop number used togenerate the digital image. In another embodiment, the color spacechannel includes a value channel of an HSV color space.

In yet another embodiment, the method includes the step of downsamplingthe digital image. Further, the method can also include the step ofdenoising the digital image.

In one embodiment, the step of applying includes the filtered valuebeing an nth lowest actual color space value. In this embodiment, nequals the number of pixels in the expected dust spot configuration.

The method can also include the step of generating a dust map of thedigital image that excludes any potential dust spots in a non-uniformregion of the digital image. In this embodiment, the dust map caninclude one or more potential dust spots in a uniform region of thedigital image.

The method can also include the step of determining a probability of thepresence of the one or more dust spots.

The present invention also includes a digital camera that detects a dustspot in a digital image using any of the methods previously described.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of this invention, as well as the invention itself,both as to its structure and its operation, will be best understood fromthe accompanying drawings, taken in conjunction with the accompanyingdescription, in which similar reference characters refer to similarparts, and in which:

FIG. 1 is a simplified view of an image including one or more uniformregions and one or more non-uniform regions;

FIG. 2 is a flow chart of one embodiment of a method having steps of thepresent invention for detecting a dust spot in a digital image;

FIG. 3A is a diagram of a filter window including an expected dust spotconfiguration formed from a plurality of pixels;

FIG. 3B is a diagram of a portion of the digital image including thefilter window illustrated in FIG. 3A, with each pixel having a numericalcolor space value;

FIG. 3C is a table including non-sorted and sorted color space valuesfor each pixel in the filter window in FIG. 3B,

FIG. 3D illustrates the color space values after a statistic orderfilter has been applied to each pixel in the image in FIG. 3B;

FIG. 3E is a simplified view of a binary image of a potential dust spotfollowing application of a statistic order filter;

FIG. 3F is a diagram of a linear pillbox filter that is applied to thebinary image to evaluate how each pixel matches a binary template of theexpected dust spot configuration;

FIG. 3G illustrates an array showing the probability of the presence ofa dust spot at any given pixel location within the image in FIG. 3B;

FIG. 3H is a representation of a binary image that is created based on apre-selected confidence threshold, as applied to FIG. 3G;

FIG. 4A is a simplified view of a dust map including a plurality ofpotential dust spots prior to post-processing;

FIG. 4B is a simplified view of a dust map including a plurality ofpotential dust spots and a mask that covers all potential dust spots inthe non-uniform regions during post-processing;

FIG. 4C is a simplified view of a final dust map including a pluralityof dust spots detected at a first confidence level; and

FIG. 4D is a simplified view of a final dust map including a pluralityof dust spots detected at a second confidence level.

DESCRIPTION

The methods provided herein can determine the probability of thepresence of one or more dust spots in a digital image. These methods canbe incorporated into a digital camera. Alternatively, these methods canbe utilized by a processing apparatus that is separate from the digitalcamera.

FIG. 1 is a simplified representative view of a digital image 10 (alsosometimes referred to herein as an “image”) which has been taken by adigital camera (not shown). In this embodiment, the digital imageincludes a uniform region 12 and a non-uniform region 14. In oneembodiment, the uniform region 12 can be any relatively uniformbackground, such as a sky or a blank wall, as non-exclusiverepresentative examples. Further, in one embodiment, the non-uniformregion 14 can include any images that are generally non-uniform inappearance, such as buildings, people or trees, as non-exclusiverepresentative examples. The image 10 illustrated in FIG. 1 alsoincludes a plurality of potential dust spots 16 which are positionedwithin both the uniform region 12 and the non-uniform region 14. Themethods provided herein are used to determine whether the potential dustspots 16 on the image 10 are likely actual dust spots which can beremoved and/or otherwise addressed by the camera, or by otherpost-processing apparatuses.

FIG. 2 is a flow chart of one embodiment of a method having steps of thepresent invention for detecting a dust spot in a digital image. Some ofthe steps outlined in the embodiment in FIG. 2 can be omitted dependingupon the desired degree of accuracy. Stated another way, not all of thesteps shown and described relative to FIG. 2 are essential to carryingout the intent of the present invention.

At step 220, the image to be analyzed can be reduced, i.e. downsampled,if desired. By downsampling the image, some or all of the subsequentcomputations can be expedited due to a reduced number of pixels thatwould comprise the image. In one embodiment, the image can bedownsampled by approximately 50% in each direction (length and width) sothat the downsampled image has approximately 25% of the area of theoriginal image. In alternative embodiments, the downsampled image canhave greater than or less than 25% of the area of the original image.

At step 222, the image can be converted from a RGB (red, green, blue)space to a color space that separates a luminance component of theimage. For example, in one non-exclusive embodiment, the image can beconverted to an HSV color space. As used herein, HSV stands for hue,saturation and value channels. In another non-exclusive embodiment, theimage can be converted to an HSL color space (hue, saturation andlightness channels). In still other alternative embodiments, the imagecan be converted to an HSI (hue, saturation, intensity channels) or HSB(hue, saturation, brightness channels) color space. In otherembodiments, the image can be converted to yet another suitable type ofcolor space. Although the embodiments described in greatest detailherein focus on conversion to HSV color space, it is recognized thatconversion to any other suitable color space can similarly beaccomplished while still carrying out the intent of the presentinvention.

At step 224, denoising can be applied to the image to improve theperformance of the described methods. In one embodiment, the denoisingcan include using a linear low pass filter. Alternatively, other knownmethods of denoising can be utilized.

At step 226, an expected dust spot configuration 344 (illustrated inFIG. 3A, for example) is determined. The expected dust spotconfiguration 344, which can include the size and/or shape of the dustspot, can be determined by analyzing one or more camera settings, suchas an f-stop number that was used to obtain the image, as onenon-exclusive example. Depending on the specific f-stop number used, theexpected dust spot configuration 344, such as the average diameter innumber of pixels, can be determined via a look-up table or by anothersuitable method or calculation. Other non-exclusive factors can includethe model and/or type of camera that was used to obtain the image, forexample.

At step 228, a statistic order filter is applied to a color spacechannel of the image. In one embodiment, following conversion from RGBto HSV, the statistic order filter is applied to the value channel. Inan alternative embodiment, the statistic order filter is applied to aluminance channel, such as lightness, brightness or intensity, etc. Afilter window 346, such as the 7×7 pixel filter window 346 illustratedin FIG. 3A, is utilized that is somewhat larger than the expected dustspot configuration 344. An output (sometimes referred to herein as a“filtered value”) of the statistic order filter is the nth lowest valuewithin the filter window 346, where n is some predetermined valuederived from the expected dust spot configuration 344. With this design,n is a parameter of the statistical order filter that is derived fromthe expected dust spot configuration 344.

Referring to FIG. 3A, in one somewhat simplified example, the expecteddust spot configuration 344 can have a shape and size similar to thatillustrated by pixels within a shaded area of FIG. 3A (onerepresentative pixel is labeled 348 in FIG. 3A). In this example, thefilter window 346 has a 7×7 pixel configuration, although it isrecognized that the filter window 346 can be any suitable size orconfiguration, provided the filter window 346 is at least as large asthe expected dust spot configuration 344.

FIG. 3B illustrates that each pixel 348 (only one pixel 348 is labeledin FIG. 3B for clarity) within an image 310 (or portion thereof) canhave a color space value that is based on the image value channel. Theimage 310 represented in FIG. 3B includes the 7×7 pixel filter window346 (which is shown with diagonal cross-hatching in FIG. 3B). The pixelfor which the filtered value (V_(filtered)) is currently computed 349 isshown with double cross-hatching in the center of the filter window 346in FIG. 3B. The color space values are indicated within each pixel 348in FIG. 3B. As provided previously, in one embodiment, the output of thestatistic order filter for the pixel currently being evaluated 349 isdetermined by the n^(th) lowest color space value within the filterwindow 346, where n equals the number of pixels in the expected dustspot configuration 344. In this example, n=13 because the expected dustspot configuration 344 (illustrated in FIG. 3A) contains thirteen pixels348.

FIG. 3C is a table that provides an unsorted and a sorted listing of allof the color space values within the 7×7 pixel filter window 346illustrated in FIG. 3B. In the embodiment illustrated in FIG. 3B, the13^(th) lowest color space value is 39 (shown in a rectangular box inFIG. 3C), which would be the output of the statistic order filter forthe pixel currently being evaluated 349 (illustrated in FIG. 3B). Aseach pixel 348 is evaluated, the filter window 346 in FIG. 3B wouldshift accordingly so that the pixel being evaluated 349 is in the centerof the shifted filter window, such as that shown in FIG. 3B. Statedanother way, if the next successive pixel being evaluated were one pixeldirectly to the right of pixel 349 in FIG. 3B, the entire filter window346 would likewise shift directly to the right by one pixel. Thisprocess continues for each pixel being evaluated in a particular image310.

Referring back to FIG. 2, at step 230, the filtered value (V_(filtered))at each pixel location is compared to the actual color space value (suchas the value channel value) on a pixel-by-pixel basis for each pixel 348in the image. The filtered value (V_(filtered)) is used to threshold thevalue channel for each pixel, as follows:B(i,j)=1 if V(i,j)≦V _(filtered)(i,j),andB(i,j)=0 if V(i,j)>V _(filtered)(i,j).where B(i,j) is a binary number for the particular pixel at coordinates(i,j) in the image, V(i,j) is the actual color space value for theparticular pixel at coordinates (i,j) in the filter window 346, andV_(filtered)(i,j) is the filtered value for each pixel in the image (orportion thereof) as determined previously. Therefore, in this example,each actual color space value is compared to V_(filtered)(i,j), togenerate a series of binary values B(i,j) in the configuration of thefilter window 346.

FIG. 3D illustrates the color space values after the statistic orderfilter has been applied to each pixel in the image 310 in FIG. 3B.

At step 232, a binary image 350 (illustrated in FIG. 3E) can be created.FIG. 3E is a representation of the binary image 350 that is createdbased on the comparison performed in step 230. Based on the expecteddust spot configuration 344 (illustrated in FIG. 3A), including its sizeand shape, the binary image can have any suitable dimensions and caninclude any suitable number of 0's and 1's.

In the binary image 350, the dust spots can show as an area of pixelshaving a value of 1 that has a shape that can be somewhat similar to theexpected dust spot configuration 344. Further, the dust spot issurrounded by an area of pixels having a value of 0. Areas within theimage that do not contain a dust spot look either as a random mixture of1's and 0's or display somewhat of a different shape than the expecteddust spot configuration 344, as explained in greater detail below.

At step 234, the binary image 350 is compared to the expected dust spotconfiguration 344 to determine the likelihood of the presence of a dustspot in the image.

At step 235, a pillbox linear filter (such as that illustrated in FIG.3F) can be applied to the binary image 350 to evaluate for each pixelhow well the neighborhood of the pixel matches a binary template of theexpected dust spot configuration 344 (illustrated in FIG. 3A). As usedherein, in one embodiment, a “pillbox linear filter” can include afilter with point spread function that looks somewhat like a disk. Thedisk has a circular area wherein the value within the circular area isconstant, except for possibly some anti-aliasing of edges that helps tobetter approximate a circle in a square pixel grid. For example, in oneembodiment, when the expected dust spot configuration 344 issubstantially circular, the binary template is a substantially circulartemplate, the diameter of which is an expected dust spot diameter.

FIG. 3G illustrates an array that results from the previous step(s), andrepresents the probability of the presence of a dust spot at any givenpixel location (i,j).

FIG. 3H is a representation of a binary image 360 that is created basedon a pre-selected confidence threshold, which in this embodiment isP(i,j)≧0.7, based on the probabilities indicated in FIG. 3G. In thisembodiment, a detected dust spot is indicated by the shaded area thatcovers two adjacent pixels, each containing the number “1”. Thresholdingwith a predetermined confidence level can be used to determine whichareas are considered detected dust spots. The value of the confidencethreshold that is used can depend upon the saliency of the dust spotsthat are desired to be detected. In other words, the higher theconfidence threshold used, the more salient the dust spots that will bedetected. A lower confidence threshold will detect very weak dust spotsthat are less visible, but will also likely falsely detect dust spots inareas where there is no actual dust spot (also sometimes referred toherein as a “false positive”). Stated another way, the lower theconfidence threshold, the higher the risk of false positives.

As an example, using a first confidence threshold, such as 0.70, thebinary image 360 in FIG. 3H may be determined to be a dust spot having asize of two pixels (as illustrated by the two cross-hatched pixels inFIG. 3H). However, applying a second confidence threshold to theprobabilities indicated in FIG. 3G that is higher than the firstconfidence level, such as 0.90, the binary image 360 may show a smallerdust spot, or may eliminate the dust spot completely. Conversely, if thesecond confidence threshold is lower than the first confidencethreshold, such as 0.60, the binary image 350 may show a somewhat largerand/or diffuse dust spot.

Therefore, referring back to FIG. 2, at step 236, a determination ismade whether the probability of the presence of a dust spot is greaterthan or equal to a preselected confidence threshold. If not, at step238, a determination is made that a dust spot is not likely present atthat location in the image. If so, at step 240, a determination is madethat a dust spot is likely present at that location in the image.

At step 242, in the event that the determination is made that one ormore dust spots are likely present in the image, certain post-processingcan be performed to reduce the number of false positive results. Forexample, the process previously described can detect not only dustspots, but can also potentially detect various scene structures from thenon-uniform region 14 (illustrated in FIG. 1) of the image.

At step 244, a dust map that results from the previous step(s) can beupsampled to the original image resolution.

FIGS. 4A-4D illustrate a series of representative images prior to,during, and following one or more post-processing steps. FIG. 4A is asimplified view of a dust map including a plurality of potential dustspots prior to post-processing. In FIG. 4A, all of the potential dustspots 416 (only two of several potential dust spots 416 have beenlabeled in FIG. 4A) that have been detected using a method previouslydescribed herein have been illustrated on a dust map 452A. In thisembodiment, the dust map 452A does not include any of the non-uniformregions 14 (illustrated in FIG. 1), but instead only illustrates thelocations of the potential dust spots 416 in the image.

FIG. 4B is a simplified view of a dust map 452B including a plurality ofpotential dust spots 416 and a mask 454 (shown in cross-hatching) thatcovers all potential dust spots 416 in the non-uniform regions 14(illustrated in FIG. 1) during post-processing. In one embodiment, themask 454 is substantially similar or identical in size and shape to thestructures in the image that comprise the non-uniform region 14. In theexample illustrated herein, the non-uniform region 14 includes abuilding. In FIG. 4B, all of the potential dust spots 416 that are inthe non-uniform region 14, e.g., that fall within the area of the mask454 have been removed.

It should be noted that in the alternative to utilizing a mask 454during a post-processing step, a mask 454 can be used during apre-processing step to reduce the overall area that is analyzed by themethods provided herein. Stated another way, the detection processdescribed herein can, in one embodiment, only be applied to the uniformregion 12, which can be any relatively smooth area of the image 10. Withthis design, computation time can be reduced.

Further, in one embodiment, a pre-processing step can be incorporatedwhich determines what areas of the image 10 are uniform regions 12versus non-uniform regions 14, so that the previously describedembodiment can be better implemented. Any recognized method that isknown in the art can be utilized for segregating the uniform region 12from the non-uniform region 14.

FIG. 4C is a simplified view of a final dust map 452C with the mask 454illustrated in FIG. 4B omitted. In this embodiment, the final dust map452C includes a plurality of dust spots 456 detected at a firstconfidence threshold. Stated another way, the threshold used fordetermining the degree of similarity between the binary image 350(illustrated in FIG. 3E) and the expected dust spot configuration 444(illustrated in FIG. 4A) can be adjusted to suit the needs of the user.In the example illustrated in FIG. 4C, the confidence threshold for theprobability of the presence of a dust spot 456 is set at P>0.70,although it is recognized that any suitable alternative confidencethreshold can be used. In other words, in this example, the probabilityof a potential dust spot being an actual dust spot is greater than 70%.

FIG. 4D is a simplified view of a final dust map 452D with the mask 454illustrated in FIG. 4B omitted. In this embodiment, however, the finaldust map 452D includes a plurality of dust spots 456 detected at analternative second confidence threshold. In other words, in thisembodiment, a different confidence threshold from that describedrelative to FIG. 4C is utilized. The confidence thresholds described inFIGS. 4C and 4D are intended to represent two alternative thresholds.Stated another way, it is not necessary nor required that any two ormore confidence thresholds be utilized with the methods provided herein.Thus, in these embodiments, only one confidence threshold is utilized.Alternatively, more than one confidence threshold can be used, and theresults of using different confidence thresholds can be compared to oneanother, if desired.

In the example illustrated in FIG. 4D, the confidence threshold for theprobability of the presence of a dust spot 456 is set at P>0.90. Inother words, in this example, the probability of a potential dust spotbeing an actual dust spot is greater than 90%. Because the secondconfidence threshold is higher than the first confidence threshold, amore discriminate determination of the presence of a dust spot occurs.Thus, in FIG. 4D, fewer dust spots 456 are included on the dust map452D. However, the actual existence of these dust spots is more likelythan the dust spots illustrated in the dust map 452C in FIG. 4C.

In the embodiments illustrated and described relative to FIGS. 4C and4D, it is recognized that by setting a different value of the threshold,the sensitivity of the method and the risk of detecting false positivescan be manipulated. Additionally, these post-processing steps can beperformed to accomplish one or more of determining (i) whether agradient in the value channel exceeds a certain threshold, i.e. becausedust spots are blurry, the gradient is relatively low; if the gradientis relatively high, the potential dust spot is more likely to be part ofthe image content than an actual dust spot; and/or (ii) whether thegradient in hue channel is small enough (because a dust particleinfluences mostly the value (or luminance) while the hue remainssubstantially unchanged, if the area is not substantially constant inthe hue channel, the potential dust spot is more likely to be part ofthe image content than an actual dust spot.

While a number of exemplary aspects and embodiments have been discussedabove, those having skill in the art will recognize certainmodifications, permutations, additions and sub-combinations thereof. Itis therefore intended that the following appended claims and claimshereafter introduced are interpreted to include all such modifications,permutations, additions and sub-combinations as are within their truespirit and scope.

What is claimed is:
 1. A method for detecting a dust spot in a digitalimage having a plurality of pixels, the method comprising the steps of:determining an expected dust spot configuration of the dust spot in theimage; applying a statistic order filter to a color space channel foreach of the plurality of pixels in the digital image to generate afiltered value for each of the plurality of pixels, the filtered valuebeing an nth lowest actual color space within a filter window thatencircles the expected dust spot configuration, wherein n is a parameterof the statistic order filter that is based on the number of pixels inthe expected dust spot configuration so that the filtered value is basedat least partially upon the expected dust spot configuration; andcomparing the filtered value to an actual color space value of each ofthe plurality of pixels to generate a binary image of the digital image.2. The method of claim 1 further comprising the step of comparing thebinary image to the expected dust spot configuration to determine aprobability of the presence of the dust spot in the digital image. 3.The method of claim 1 wherein the step of determining includes basingthe expected dust spot configuration at least partially on a camerasetting on a camera used to generate the digital image.
 4. The method ofclaim 1 wherein the step of determining includes basing the expecteddust spot configuration at least partially on an f-stop number used togenerate the digital image.
 5. The method of claim 1 wherein the colorspace channel includes a value channel of an HSV color space.
 6. Themethod of claim 1 further comprising the step of downsampling thedigital image prior to the step of determining.
 7. The method of claim 1further comprising the step of denoising the digital image prior to thestep of determining.
 8. The method of claim 1 wherein the step ofapplying includes n being equal to the number of pixels in the expecteddust spot configuration.
 9. The method of claim 1 further comprising thestep of generating a dust map of the digital image that excludes anypotential dust spots in a non-uniform region of the digital image. 10.The method of claim 9 wherein the step of generating includes the dustmap including one or more potential dust spots in a uniform region ofthe digital image.
 11. The method of claim 10 further comprising thestep of generating includes determining a probability of likelihood ofthe presence of the one or more dust spots.
 12. The method of claim 1further comprising the step of converting the digital image to an HSVcolor space prior to the step of applying.
 13. A digital camera thatdetects a dust spot in a digital image using the method of claim
 1. 14.A method for detecting a dust spot in a digital image having a pluralityof pixels, the method comprising the steps of: determining an expecteddust spot configuration of the dust spot in the image; applying astatistic order filter to a color space channel for each of theplurality of pixels in the digital image to generate a filtered valuefor each of the plurality of pixels, wherein the filtered value is annth lowest actual color space value within a filter window thatencircles the expected dust spot configuration, wherein n is derivedfrom the expected dust spot configuration so that the filtered value isbased at least partially upon the expected dust spot configuration;comparing the filtered value for each of the plurality of pixels to anactual color space value of each of the plurality of pixels to generatea binary image of the digital image; and comparing the binary image tothe expected dust spot configuration to determine a probability of thepresence of the dust spot in the digital image.
 15. The method of claim14 wherein the step of determining includes basing the expected dustspot configuration at least partially on a camera setting on a cameraused to generate the digital image.
 16. The method of claim 14 whereinthe step of determining includes basing the expected dust spotconfiguration at least partially on an f-stop number used to generatethe digital image.
 17. The method of claim 14 further comprising thestep of downsampling the digital image prior to the step of determining.18. The method of claim 14 further comprising the step of denoising thedigital image prior to the step of determining.
 19. The method of claim14 wherein the step of applying includes n being equal to the number ofpixels in the expected dust spot configuration.
 20. The method of claim14 further comprising the step of generating a dust map of the digitalimage that excludes any potential dust spots in a non-uniform region ofthe digital image.
 21. The method of claim 20 wherein the step ofgenerating includes the dust map including one or more potential dustspots in a uniform region of the digital image.
 22. The method of claim21 further comprising the step of generating includes determining aprobability of likelihood of the presence of the one or more dust spots.23. The method of claim 14 further comprising the step of converting thedigital image to an HSV color space prior to the step of applying.
 24. Adigital camera that detects a dust spot in a digital image using themethod of claim
 14. 25. A method for detecting a dust spot in a digitalimage having a plurality of pixels, the method comprising the steps of:downsampling the digital image; denoising the digital image; determiningan expected dust spot configuration of the dust spot in the digitalimage, the expected dust spot configuration being based at leastpartially on an f-stop number used to generate the digital image;applying a statistic order filter to a value channel of an HSV colorspace for each of the plurality of pixels in the image to generate afiltered value for each of the plurality of pixels, the filtered valuebeing an nth lowest actual color space value within a filter window thatencircles the expected dust spot configuration, where n is approximatelyequal to the number of pixels in the expected dust spot configuration;comparing the filtered value to an actual color space value of each ofthe plurality of pixels to generate a binary image of the digital image;comparing the binary image to the expected dust spot configuration todetermine a probability of the presence of the dust spot in the digitalimage; generating a dust map of the digital image that excludes anypotential dust spots in a non-uniform region of the digital image, andincludes one or more potential dust spots in a uniform region of thedigital image; and determining a probability of likelihood of thepresence of the one or more dust spots.
 26. The method of claim 14wherein applying includes the color space channel including a luminancecomponent.